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Some Imputation Methods to Treat Missing Values in Knowledge Discovery in Data warehouse
Dr. Diwakar Shukla, Rahul Singhai
Pages - 1 - 13     |    Revised - 30-06-2010     |    Published - 10-08-2010
Volume - 1   Issue - 2    |    Publication Date - July 2010  Table of Contents
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KEYWORDS
Data Preprocessing, Data Mining, Missing Values, Imputation, data cleaning, data reduction
ABSTRACT
One major problem in the data cleaning & data reduction step of KDD process is the presence of missing values in attributes. Many of analysis task have to deal with missing values and have developed several treatments to guess them. One of the most common method to replace the missing values is the mean method of imputation. In this paper we suggested a new imputation method by combining factor type and compromised imputation method, using two-phase sampling scheme and by using this method we impute the missing values of a target attribute in a data warehouse. Our simulation study shows that the estimator of mean from this method is found more efficient than compare to other.
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Associate Professor Dr. Diwakar Shukla
Dr. H.S.G. Central University, Sagar (M.P.), India. - India
diwakarshukla@rediffmail.com
Mr. Rahul Singhai
Devi Ahilya University - India


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